Next Article in Journal
Exergoeconomic Analysis of Corn Drying in a Novel Industrial Drying System
Previous Article in Journal
Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction
Open AccessArticle

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911 Madrid, Spain
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(6), 688; https://doi.org/10.3390/e22060688
Received: 24 May 2020 / Revised: 17 June 2020 / Accepted: 19 June 2020 / Published: 20 June 2020
(This article belongs to the Section Multidisciplinary Applications)
This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier. View Full-Text
Keywords: depression detection; speech; convolutional neural networks; ensemble learning depression detection; speech; convolutional neural networks; ensemble learning
Show Figures

Figure 1

MDPI and ACS Style

Vázquez-Romero, A.; Gallardo-Antolín, A. Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks. Entropy 2020, 22, 688.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop